Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Michael Lentmaier; Bernhard Krach; Thanawat Thiasiriphet

Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Michael Lentmaier, Bernhard Krach, Thanawat Thiasiriphet

External Organization - Unknown

Research output: Contribution to conference › Paper, not in proceeding › peer-review

Abstract

: A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

Original language	English
Pages	1712-1721
Publication status	Published - 2007
Externally published	Yes
Event	International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007 - Forth Worth, TX, United States Duration: 2007 Sept 25 → 2007 Sept 28

Conference

Conference	International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007
Country/Territory	United States
City	Forth Worth, TX
Period	2007/09/25 → 2007/09/28

Subject classification (UKÄ)

Electrical Engineering, Electronic Engineering, Information Engineering

Free keywords

GNSS
positioning
multipath mitigation

Cite this

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title = "Dynamic Multipath Estimation by Sequential Monte Carlo Methods",

abstract = ": A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.",

keywords = "GNSS, positioning, multipath mitigation",

author = "Michael Lentmaier and Bernhard Krach and Thanawat Thiasiriphet",

year = "2007",

language = "English",

pages = "1712--1721",

note = "International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

}

Dynamic Multipath Estimation by Sequential Monte Carlo Methods. / Lentmaier, Michael; Krach, Bernhard; Thiasiriphet, Thanawat.
2007. 1712-1721 Paper presented at International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007, Forth Worth, TX, United States.

Research output: Contribution to conference › Paper, not in proceeding › peer-review

TY - CONF

T1 - Dynamic Multipath Estimation by Sequential Monte Carlo Methods

AU - Lentmaier, Michael

AU - Krach, Bernhard

AU - Thiasiriphet, Thanawat

PY - 2007

Y1 - 2007

N2 - : A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

AB - : A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops it builds upon complexity reduction concepts that previously have been applied within Maximum Likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both artificially generated random channels and high resolution channel impulse responses recorded during a measurement campaign.

KW - GNSS

KW - positioning

KW - multipath mitigation

UR - https://www.scopus.com/pages/publications/58449134194

M3 - Paper, not in proceeding

SP - 1712

EP - 1721

T2 - International Technical Meeting of the Institute of Navigation Satellite Division, (ION GNSS), 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Dynamic Multipath Estimation by Sequential Monte Carlo Methods

Abstract

Conference

Subject classification (UKÄ)

Free keywords

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Cite this